We propose a model to quantitatively estimate quote spoofing in stock exchange markets without any answer labels, for the sake of more efficient and thorough inspection. In our model, density ratio estimation is used to extract unusual trading activities in unsupervised manner. Using market data at Tokyo Exchange and judges by experts, we validate the model and the result indicates that about 50% of half-day grouped trading histories can be ignored of manual inspection with 80% of frauds in the rest half of the dataset.